Results for “ml”

GPU-accelerate Python code using CuPy, Numba CUDA, Warp, cuDF, cuML, cuGraph, and other RAPIDS libraries for dramatic speedups on numerical, data, ML, graph, and simulation workloads.

Run an AI Lab division meeting with research and build agents (no AI Startup agents). Use when the user requests to "start a lab meeting", "convene the lab", or "run a sprint retrospective for the lab".

Execute código Python na nuvem com contêineres serverless, GPUs e autoscaling. Use ao fazer deploy de modelos de ML, executar jobs de processamento em lote, agendar tarefas compute-intensivas ou servir APIs que exigem aceleração GPU ou scaling dinâmico.

High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.

Technical documentation specialist for API docs, tutorials, architecture docs, and developer experienceUse when "documentation, docs, readme, tutorial, api docs, guide, changelog, comments, openapi, documentation, api-docs, tutorials, readme, openapi, swagger, developer-experience, technical-writing, ml-memory" mentioned.

End-to-end pipeline for writing ML/AI research papers — from experiment design through analysis, drafting, revision, and submission. Covers NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Integrates automated experiment monitoring, statistical analysis, iterative writing, and citation verification.

Esta habilidade deve ser usada ao trabalhar com LaminDB, um framework de dados de código aberto para biologia que torna dados consultáveis, rastreáveis, reproduzíveis e FAIR. Use ao gerenciar datasets biológicos (scRNA-seq, espacial, citometria de fluxo, etc.), rastrear workflows computacionais, curar e validar dados com ontologias biológicas, construir data lakehouses, ou garantir linhagem de dados e reprodutibilidade em pesquisa biológica. Aborda gerenciamento de dados, anotação, ontologias (genes, tipos de célula, doenças, tecidos), validação de esquema, integrações com orquestradores de workflow (Nextflow, Snakemake) e plataformas MLOps (W&B, MLflow), e estratégias de deployment.

Run machine learning and generative AI tasks directly in BigQuery SQL using built-in functions for forecasting, anomaly detection, key driver analysis, and text generation.

High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.

API design specialist for REST, GraphQL, gRPC, versioning strategies, and developer experienceUse when "api design, rest, graphql, grpc, openapi, swagger, versioning, pagination, rate limiting, endpoint, api, rest, graphql, grpc, openapi, swagger, versioning, pagination, rate-limiting, ml-memory" mentioned.

Validate and use CUDA graph capture in Megatron Bridge, including local full-iteration graphs and Transformer Engine scoped graphs for attention, MLP, and MoE modules.

Run an AI Startup division meeting with the seven AI Startup agents (no AI Lab agents). Use when the user requests to "start a startup meeting", "convene the startup team", or "run a sprint review for the product".

Search real-time news with bias scoring, get live stock/ETF/crypto data with AI analysis, ML options pricing, balanced news synthesis, and meme search via the Helium MCP server.

Provides battle-tested PyTorch training recipes for LLMs, vision, diffusion, and biomedical domains, covering training loops, optimizer selection, LR scheduling, mixed precision, and debugging.

Assess AI/ML systems for prompt injection, jailbreak vulnerabilities, model inversion risk, data poisoning exposure, and agent tool abuse, with MITRE ATLAS mapping and guardrail recommendations.

Full-featured computational pathology toolkit. Use for advanced WSI analysis including multiplexed immunofluorescence (CODEX, Vectra), nucleus segmentation, tissue graph construction, and ML model training on pathology data. Supports 160+ slide formats. For simple tile extraction from H&E slides, histolab may be simpler.

Process large ML datasets in parallel across CPU or GPU clusters, with streaming execution, multi-format I/O, and integration with Ray Train, PyTorch, and TensorFlow for batch inference and preprocessing pipelines.

You are an expert in Soda, the data quality platform for testing, monitoring, and profiling data. You help developers write data quality checks in YAML that validate freshness, completeness, uniqueness, validity, and business rules — catching data issues before they reach dashboards and ML models.

Turn raw MATLAB code into a published .mltbx toolbox — full pipeline from scope definition through packaging and release. Drives 8 phases with human checkpoints, enforcing ordering and dependencies. Use when asked to package, create a toolbox, or run the files-to-package pipeline.

Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.

Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.

This skill guides the simultaneous measurement of serum and urine osmolarity to evaluate polyuria for central diabetes insipidus. It is triggered when a patient presents with polyuria exceeding 50 mL/kg/24 hours or 3.5 L/day in a 70‑kg individual.

Expert guidance for Mojo, the programming language by Modular that combines Python's usability with C-level performance. Helps developers write high-performance AI/ML code, optimize numerical computations with SIMD and parallelism, and gradually port Python code to Mojo for orders-of-magnitude speedups.

Patterns for running AI models locally in browsers using WebGPU, Transformers.js, WebLLM, and ONNX Runtime. Zero API costs, full privacy. Use when "on-device AI, browser AI, WebLLM, Transformers.js, WebGPU, edge inference, offline AI, client-side ML, ONNX web, " mentioned.

Code review specialist for quality standards, design patterns, security review, and constructive feedbackUse when "code review, pull request, PR review, code quality, refactor, technical debt, design pattern, best practice, code-review, quality, patterns, security, refactoring, best-practices, pull-request, review, ml-memory" mentioned.

Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard, MLflow), and distributed training (DDP, FSDP, DeepSpeed) for scalable neural network training.

High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.

Toolkit de alta performance para análise de intervalos genômicos em Rust com bindings Python. Use ao trabalhar com regiões genômicas, arquivos BED, tracks de cobertura, detecção de sobreposições, tokenização para modelos de ML, ou análise de fragmentos em genômica computacional e aplicações de aprendizado de máquina.

Deterministic pixel art upscaling using EPX/Scale2x, hq2x/hq4x, and xBR algorithms that add valid sub-pixels through pattern recognition. Activate on 'pixel art scaling', 'EPX', 'Scale2x', 'hq2x', 'hq4x', 'xBR', 'retro game upscaling'. NOT for AI/ML upscaling, photo enlargement, or simple nearest-neighbor.

Client library architect for SDK design, API ergonomics, versioning, and developer experienceUse when "sdk design, client library, api client, developer experience, sdk versioning, type generation, http client, api wrapper, sdk, client-library, api-client, developer-experience, versioning, type-safety, http-client, ml-memory" mentioned.

Analyze whole-slide pathology images with Python: load 160+ slide formats, preprocess H&E stains, segment nuclei, construct spatial graphs, train ML models, and process multiplex immunofluorescence data (CODEX, Vectra).

Runs Python code in the cloud with Modal — serverless containers, on-demand GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that need GPU acceleration or dynamic scaling. Part of the AlterLab Academic Skills suite.

Writes rigorous mathematical proofs for ML/AI theory. Use when asked to prove a theorem, lemma, proposition, or corollary, fill in missing proof steps, formalize a proof sketch, 补全证明, 写证明, 证明某个命题, or determine whether a claimed proof can actually be completed under the stated assumptions.

Google quantum computing framework. Use when targeting Google Quantum AI hardware, designing noise-aware circuits, or running quantum characterization experiments. Best for Google hardware, noise modeling, and low-level circuit design. For IBM hardware use qiskit; for quantum ML with autodiff use pennylane; for physics simulations use qutip.

Google quantum computing framework. Use when targeting Google Quantum AI hardware, designing noise-aware circuits, or running quantum characterization experiments. Best for Google hardware, noise modeling, and low-level circuit design. For IBM hardware use qiskit; for quantum ML with autodiff use pennylane; for physics simulations use qutip.

High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.